explaining the gap between policy aspirations and

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CIMR Research Working Paper Series

Working Paper No. 20

Explaining the gap between policy aspirations and implementation:

The case of university knowledge transfer policy in the United Kingdom

by

Ainurul Rosli University of Wolverhampton

University of Wolverhampton Business School MN Building, Nursery Street, Wolverhampton, WV1 1AD, UK

[email protected]

Federica Rossi Birkbeck, University of London

School of Business, Economics and Informatics Malet Street, London WC1E 7HX

[email protected]

29/11/2014

ISSN 2052-062X

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Explaining the gap between policy aspirations and implementation:

The case of university knowledge transfer policy in the United

Kingdom

Ainurul Rosli *

Federica Rossi +

Abstract

Implementing policies that build upon the generic aspirations set out in government documents can be

a challenging task for implementation agencies. We argue that, particularly when policies deal with

complex and ambiguous issues, an increasing gap may open up between government-set objectives and

the instruments used for policy implementation and evaluation, as the former are characterized by

increasing breadth and ambiguity, while the latter become progressively narrower in scope.

We propose a conceptual framework to explain why these processes may occur, and, as an example,

we analyze the policies in support of university-industry knowledge transfer in the United Kingdom.

Their evolution shows how the government’s policy aspirations have become broader, while

implementation has increasingly relied upon instruments that depend on narrowly defined measures of

output attainment. The result is a system of performance measurement and funds allocation that is quite

far from the government’s aspirations.

Keywords: Policy implementation, policy evaluation, knowledge transfer, university-industry

interactions, Higher Education Business and Community Interaction Survey, Higher Education

Innovation Fund

* University of Wolverhampton Business School, University of Wolverhampton, MN Building, Nursery

Street, Wolverhampton, WV1 1AD, UK, E-mail: [email protected]

+ School of Business, Economics and Informatics, Birkbeck, University of London, Malet Street,

London, WC1E 7HX, UK, E-mail: [email protected]

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1. Introduction

Implementing policies that build upon generic aspirations set out in government

documents (such as white papers and reviews) can be a challenging task for

implementation agencies (Bach et al, 2014; Flanagan et al, 2011). Especially when the

policy concerns complex issues characterized by a high level of ambiguity, the gap

between the government’s aspirations and the instruments used in policy

implementation can widen over time, leading to an increasing mismatch between

them. This is due, we argue, to pressures in different directions – government

aspirations become broader and more ambiguously defined, while implementing

agencies increasingly benchmark their targets to the achievement of narrowly defined

outputs (often quantitative indicators) which are usually very loosely related, if at all,

to the government’s objectives.

We structure our explanation of the processes underpinning the growing gap between

policy aspirations and implementation on the basis of a conceptual framework, which

builds upon and integrates several arguments from the literature on policy

implementation. Then, in order to illustrate how these processes can play out in

practice, we present the case of policies in support of university-industry knowledge

transfer in the United Kingdom (UK). We highlight the evolution of policymakers’

aspirations through a qualitative meta-synthesis of policy documents issued since the

1990s, and we map the parallel evolution of two key policy instruments supporting

university knowledge transfer in this country: the Higher Education Innovation Fund

(HEIF), which is allocated to universities on the basis of their knowledge transfer

performance, and the Higher Education Business and Community Interaction

(HEBCI) survey which is used to determine how the above mentioned fund should be

allocated.

The paper is organized as follows. In section 2, we discuss some theoretical issues

surrounding policy implementation and the process through which general policy

aspirations are formulated and articulated into specific initiatives; we then propose a

conceptual framework to explain the growing gap between policy aspirations and

implementation. In section 3 we describe the methodology. In section 4, we illustrate

the growing gap between policy aspirations and implementation by: first, exploring

the evolution of government policies for university-industry knowledge transfer in the

UK since the 1990s, through a review of the policy and research documents and the

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policy initiatives that have shaped the creation of a permanent stream of funding to

support university-industry knowledge transfer; and, second, examining the evolution

of the HEIF and of the HEBCI instruments. Section 5 presents some general

implications of our analysis for policy development.

2. Explaining the growing gap between policy aspirations and implementation

2.1. Key issues in policy implementation

Policy implementation focuses on the relationship between the expression of the

government’s intention to do something (or to stop doing something) and the actual

result obtained (O’Toole, 2000). While the process of policy implementation is

crucial for the attainment of policy objectives, its study has not led to generalizable

theories regarding the factors for achieving success (Lee, 2011; Linton, 2002) and has

often remained marginal in policy studies (Robichau and Lynn, 2009; Sabatier, 2007)

so much so that much greater understanding of the nature of policy implementation

processes is still needed in order to help policymakers devise the appropriate

instruments to reach their objectives (Kapsali, 2011).

Two main alternative approaches to implementation have been highlighted. Top-

down approaches assume that policy objectives can be fully specified by

policymakers, and that successful implementation can be carried out through the set

up of appropriate instruments. This centralized perspective emphasizes the ability to

control actors through coercive and normative means. It ignores the role of local

agency on the part of actors implementing the policy; the focus is on administrative

processes, and disregards the political aspects of implementation (Matland, 1995; Van

Meter et al., 1975). It has been suggested that this approach often leads to failure in

implementation due to the unrealistic expectations that the actors involved in the

implementation will behave as prescribed, whereas in practice the top-down

imposition of objectives and processes often leads to resistance, disregard or pro-

forma compliance on the part of local actors (Berman, 1980, Mole, 2002).

On the other hand, bottom-up approaches pay attention to the objectives, strategies,

activities and formal and informal relationships between the actors tasked with

implementing the policy and seek to exploit them in order to structure actions at the

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local level. A recent development in this perspective involves the study of the

networks of relationships between the actors involved (Holman, 2008; Linton, 2000;

Meek, 2005) in order to investigate patterns of behaviour, analyse interdependencies

and construct best practice models (Calia et al., 2007), an approach which however

has not yet led to precise normative directions for implementation (Kapsali, 2011). A

problem with bottom up approaches is that allowing too much autonomy at the local

level may lead actors to pursue individual goals at the expense of the overall policy

objective (Matland, 1995).

Moving beyond the dichotomy between top-down and bottom-up approaches, third-

generation implementation approaches have attempted to propose a synthesis of both

perspectives (Barrett, 2004). Sabatier (1986, 2007) argued for the importance of

policy learning and highlighted the need for policy to be analysed in cycles of more

than 10 years. Elmore (1982 and 1985) proposed an iterative model of

implementation according to which general objectives are set but actual

implementation tools are adapted and redesigned according to the specific problems

emerging from the local level.

Several authors have argued that, in the course of policy implementation, general

aspirations are expressed in the form of objectives, or expected outcomes, of the

policy. Outcomes measure efficacy (Omachonu and Nanda, 1989) in terms of the

results generated by intervention (Schalock and Bonham, 2003), usually captured by

changes in behaviour and performance (Patton, 1997). It has also been observed that

in practice the focus on outcomes is often replaced by a focus on outputs (Robichau

and Lynn, 2009). Outputs do not measure the actual changes in behaviour and

performance that result from the intervention, but measure the quantity, quality, and

timeliness of the goods and services that are the tangible result of an intervention. As

such, they are far easier to measure compared to outcomes, which are often intangible

(Omachonu and Nanda, 1989).

However, the extent to which the outputs that are being measured support the

intended outcomes is debatable: sometimes, outputs and outcomes are only loosely

related because, for the sake of measurability, information about intangible outcomes

is not captured. A comprehensive approach to explaining the gap between policy

objectives and implementation has been proposed by Matland (1995) who suggested

that four types of implementation process are possible according to the degree of

ambiguity and conflict surrounding it: administrative implementation (with low

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ambiguity and low conflict) where the outcome is determined by resources; political

implementation (with low ambiguity and high conflict) where the outcome is decided

by power; experimental implementation (with high ambiguity and low conflict) where

contextual decisions dominate the process, and finally symbolic implementation (with

high ambiguity and high conflict) where the strength of local level coalitions

determines the outcome. Matland argues that, while in situations of low ambiguity

and low conflict the process of implementation can be seen as linear, with a strong

ability of the government to direct the process by issuing top-down normative

constraints on behaviour, under conditions of greater ambiguity and conflict the

government is not able to provide such direction, and negotiations among the different

stakeholders involved in the implementation process take on greater importance.

With ambiguous objectives and ambiguous means and with high level of conflict,

policy implementation can be reduced to the pursuit of targets increasingly defined by

limited sets of quantitative indicators, which become “symbols” of complex policy

objectives. The crystallization of discussion around a limited number of quantitative

measures provides a way to overcome the parties’ conflicting objectives, as the

indicators are sufficiently detached from these objectives to appear uncontroversial.

The precision of the indicators also provides a way to overcome ambiguity, even

though this occurs at the expense of the possibility to check whether actual objectives

are being achieved.

Interestingly, Molas-Gallart and Castro-Martinez (2007) have suggested that such

processes are at play precisely in the case of university knowledge transfer policies

such as those that we analyse in this study: they argue that the context in which these

policies are developed is characterized by high ambiguity and high conflict, and that

indicators become the symbols around which the implementation discussion

converges.

2.2. Explaining the growing gap between policy aspirations and implementation: a

conceptual framework

We provide a conceptual framework, building and integrating several arguments from

the literature on policy implementation, to explain the growing gap between policy

aspirations and implementation. While previous studies on policy making often focus

on either agenda setting or implementation (Zahariadis, 2007), we attempt to explain

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how both processes can develop in different directions, leading to a growing

mismatch between them. The framework is illustrated in Figure 1. An explanation of

the framework follows below.

Figure 1: A framework explaining the gap between policy aspirations and implementation

The framework compares the tasks and focus of two general categories of actors, the

government (which could be at different levels, for example national, regional or

local) that sets the policy objectives, and the agency (or agencies) charged with

implementing the policy through the setting up of appropriate instruments. In fact,

while the policy process involves a complex set of elements that interact over time

with multiple levels of agency (Sabatier, 2007), it is generally possible to identify two

main centres of agency with different functional focus, either policy development or

implementation. Each of these centres can comprise, naturally, more than one

organization.

The framework suggests that, over time, the definition of objectives and the setting up

of instruments are subject to different pressures, which lead to increasing mismatch

between them, especially when the policy concerns complex and ambiguous issues

(Zahariadis, 2007): on the one hand, the objectives, and related outcomes, defined by

government defines become more broader, while on the other hand, the policy

implementation focuses on achieving outputs that are progressively narrower in

scope.

source: government specific0agencies

task: providing0direction implementationsetting0objectives setting0instruments

focus: outcomes outputs

ambiguity time time practicalityconflicting0goals conflict0managementpolitical0pressures resource0constraints

mismatchincreased0breadth reduced0scope

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The left hand side of the figure illustrates the process of agenda setting on the part of

the government. When the policy concerns complex issues, whose unfolding depends

on the many actions and interactions of multiple stakeholders acting at different

ontological levels (for example, when the processes that the policy intends to affect

depend on the actions of - and the interactions between - individuals, organizations,

and institutions) ambiguity is likely to be high (Mccreadic et al, 2008). Feldman

defines ambiguity as “a state of having many ways of thinking about the same

circumstances or phenomena” (1989, p.5). This is usually accompanied by a high

degree of interpretive flexibility, where each actor can perceive the issue differently in

time and place (Flanagan et al., 2011; Rametsteiner and Weiss, 2006). Unlike

uncertainty, ambiguity does not reduce when more information becomes available

(Wilson, 1989). More information makes understanding more complex and provides

more room for alternative interpretations. The more is known about the issue, the

more ambiguous the understanding becomes, and different views and interpretations

may emerge.

Political pressures may also set in to take into account the interests of previously

unaccounted-for stakeholders that are somehow affected by the policy issue under

consideration (Ouyang, 2006) whose interests and objectives may be mutually

conflicting. To accommodate, and possibly reconcile, different and contrasting views

and interests about the issue that are emerging over time, the policymaker sets

increasingly broad and ambiguous objectives: that is, the objectives, and the related

expected outcomes, are expressed in increasingly vague and abstract terms, they are

more broadly defined, and at times they can be contradictory (Zahariadis, 2007).

The right hand side of the figure illustrates the process of instrument definition on the

part of the implementation agency. The task of setting up appropriate policy

instruments when policy objectives are broad and ambiguous is particularly difficult.

In the course of implementation the focus on achieving policy objectives, and related

outcomes is often replaced by a focus on outputs, that is, on achieving results that are

measurable in terms of quantity, quality, and timeliness of the goods and services

delivered (Robichau and Lynn, 2009). Typically, the outputs considered tend to be in

the form of quantitative indicators (Molas-Gallart and Castro-Martinez, 2007).

The focus on outputs fulfils a political role, since it allows for some implementation

decisions to be taken even when the guidance provided by the government is

ambiguous (Nohstedt and Hansen, 2010); Zahariadis, 2007). Moreover it allows the

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parties to overcome the presence of conflicting goals by focusing on measures that are

easy to define and implement and appear objective and uncontroversial (Ackill, et al,

2013; Matland, 1995; Molas-Gallart and Castro-Martinez, 2007; Sharkansky, 2002).

However, the decisions that will be taken are likely to be far from the stated policy

objectives: indeed, Zahariadis (2005) provided examples of implementation systems

that, with a higher level of objective ambiguity, dramatically reduce efficiency of

delivery.

Over time, the scope of the outputs that underpin the policy instruments may be

narrowed down even further: the role of quantitative indicators in the implementation

process may become increasingly central, and the number and variety of indicators

considered may be reduced. The focus on progressively narrower outputs is due, on

the one hand, to the above-mentioned need to take some implementation decisions

despite the increasing breadth and ambiguity of the policy objectives. On the other

hand, it also responds to powerful practical concerns with convenience and cost

effectiveness. Implementation agencies face constraints in the amount of financial and

cognitive resources they can dedicate to their tasks. Implementing ‘holistic’ policy

approaches requires processing a lot of information, which is costly in terms of the

time and people involved (Kirk et al., 2007): the use of simple output indicators as

measures of result achievement makes it easier to collect information and to

implement incentive and reward systems based on more streamlined mechanisms,

which require fewer financial and cognitive resources. Once these indicators are in

place, their use often persists over time. In fact, agencies tend to use techniques and

approaches that they already know, drawing on common usage and capabilities and

favoured approaches: this leads them to consider relatively few “manageable” options

(Kirk et al., 2007) and results in path dependency in the chosen solutions.

In Section 4, we present a case study on the policies in support of university-industry

knowledge transfer in the UK, which illustrates the growing gap between policy

aspirations and implementation. We employ a qualitative meta-synthesis approach to

contextually show how the policy aspirations outlined by the government have been

stated in increasingly broad and ambiguous terms, while the implementation process

has increasingly relied upon the use of quantitative indicators and streamlined

procedures with progressively narrower scope. This has resulted in an increasing

mismatch between the government’s broader aspirations and the narrower approaches

used to achieve them (Bach et al, 2014; Kapsali, 2001).

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3. Methodology

In order to illustrate how the framework presented in the previous section explains the

growing gap between policy aspirations and implementation, we performed a case

study on policies supporting knowledge transfer for universities in the UK. We

scrutinized 59 different policy documents, produced by different organizations, with

different motives and incentives, in order to achieve maximum variation sampling

(Bowen, 2009; Weed, 2008).

The documents, mentioned in the national archives and HEFCE website, were

selected through a systematic literature review, which provided a ‘guiding tool’ (Lee,

2009) that allowed us to shape the search according to our research focus and

objectives. We considered policy documents published by the Department for Trade

and Industry (DTI, 1970-2007), the Office of Science and Technology (OST, 1992-

2007), the Department for Education and Skills (DFES, 2001-2007), the Department

for Innovation, Universities and Skills (DIUS, 2007-2009), the Department for

Business, Enterprise and Regulatory Reform (BERR, 2007-2009), the Department for

Business Innovation and Skills (BIS, since 2009), as well as documents published by

the Higher Education Funding Council for England (HEFCE), the agency charged

with the implementation of policies in support of universities’ knowledge transfer

activities.

The first set of documents we analysed includes 10 commissioned independent

academic research reports that raised issues, provided evidence and gave general

policy recommendations. The second set of documents, policy reviews (12 documents

issued since 1998), addressed a more specific identification of problems shaped in

policy-relevant terms: this is where evidence was interpreted in order to set general

policy objectives. The third set of documents consists of government white papers (9

documents), which provided the government’s response and reformulation of such

policy objectives, by announcing specific actions in line with their interpretation of

what the policy objectives were. The final set consists of 28 documents and news

releases that defined the details of the policies’ implementation. .

We then followed a qualitative meta-synthesis approach in analysing the documents

(Timulak, 2009) in order to understand the meaning in the context (Mishler, 1979).

This is important to provide an avenue for looking for commonalities (Finfgeld, 2003;

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Thorne et al, 2004) and contrasts (Bowen, 2009; Paterson et al., 2001) while not

overlooking the context. To perform this qualitative meta-synthesis, we followed a

descriptive interpretative strategy (Elliott and Timulak, 2005) and paid particular

attention to: the objectives mentioned in the policy documents issued by the

government; the objectives and instruments mentioned in the documents issued by

implementation agencies; changes in the actual instruments used to drive and assess

the policies, starting from the mid-1990s. We obtained some useful insights into how

policy objectives have become broader and more ambiguous over time, offering

opportunities for diverging interpretations at several points.

In order to address the notion of refocusing of outcomes onto outputs, and their

narrower scope over time, we analyzed the evolution of the HEBCI survey and of the

HEIF allocation system since they were first introduced in the late 1990s. This

analysis allowed us to illustrate how these instruments evolved to become narrower in

scope.

4. University-industry knowledge transfer policy in the UK: the evolution of

objectives and instruments

4.1. The challenges for university-industry knowledge transfer policy

In today’s economic environment, universities are required to collaborate with

industry in order to create value that is able to impact the economy and society at

large (Grady and Pratt, 2000). By enhancing university-industry collaboration, it is

argued, the new knowledge economy is able to accelerate the creation and distribution

of knowledge to a new level (Howlett, 2010; Vorley and Lawton-Smith, 2007). The

emergence of the “second academic revolution” (Etzkowitz, 2003) - where the

university’s engagement in knowledge transfer has become a “third mission” (Nelles

and Vorley, 2010) on a par with its traditional teaching and research missions - is

largely due to the government supporting and even strengthening the links between

universities, industry, and society at large.

Policymakers are currently seeking ways to make knowledge transfer between

universities and industry more effective. However, in doing so they encounter several

challenges. First, a growing number of studies showcase a wide variety of

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mechanisms through which knowledge can be transferred to external parties (Bekkers

and Bodas Freitas, 2008; Hewitt-Dundas, 2012; Laursen and Salter, 2004; Lawton-

Smith, 2007). The key policy challenges lies in determining which of these

mechanisms should be encouraged, and how to promote knowledge transfer support

initiatives that do not hamper some knowledge transfer channels while promoting

others. Second, if universities are to be incentivised to engage in their third mission

activities, it is important to establish ways to assess and reward universities’

performance in this area. What approaches can be used to measure engagement and

success? To what extent do they cover the wide variety of mechanisms through which

knowledge can be transferred, and generate appropriate incentives for universities

(Rossi and Rosli, 2014)?

Overall, there is a need for synergy between government policy aspirations, which

promote and shape the state of the university knowledge transfer eco-system, and the

policy interventions that are implemented in practice. However, our analysis shows

that the gap between aspiration and implementation in knowledge transfer policy has

actually widened over time.

4.2. The evolution of government’s policy aspirations: increasing breadth

The UK government’s concern with supporting university-industry collaboration

began in the late 1970s, when a widespread debate on the UK’s presumed failure to

exploit research emerged (Grady and Pratt, 2000). Initial interventions to answer the

problem were fragmented, without any synergies among government, university and

industry. There was a need for clarity on the aspirations of the government, and in

1993 a white paper titled “Realising our potential” (OST, 1993) was published, which

highlighted a gap between the UK’s excellence in science and technology and its

relative weakness in exploiting them to economic advantage (OST, 1993) and

emphasized the importance of partnerships between industry, government and the

science base. The white paper led to a re-configuration of government support for

science and technology. The move of the Office of Science and Technology from the

Cabinet Office to the Department for Trade and Industry in 1995 provided an avenue

to a more coordinated national policy on technology and knowledge transfer. This

white paper also inspired a rationalization, towards the end of the 1990s, of various

Government Departments’ funding schemes to support university-business

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interaction, into a single funding stream supporting universities in the development of

knowledge transfer activities, which became permanent in the early 2000s.

The election of the Labour government in 1997 saw a renewed focus on the building

up of research infrastructure, with an increase in capital investment following years of

dwindling investment in this area1 (Grady and Pratt, 2000; Lupton et al., 2013). Also,

concern for improved economic competitiveness and social welfare led to the support

for universities’ engagement with business and the community.

The Dearing Report (National Committee of Enquiry Into Higher Education, 1997),

which was the first major review in 35 years of the UK’s higher education system,

stressed the importance of partnerships between university and industry, requiring

universities to be responsive towards industry engagement, especially in

commercialising science. The report envisaged that there was a need for a more

flexible, accessible approach to business engagement, and identified a number of core

services that universities could provide to business to encourage knowledge transfer

(Howlett, 2010). This academic transition identified universities as the central focus

for economic development (Etzkowitz and Leydesdorff, 2000). The white paper “Our

Competitive Future: Building a Knowledge Driven Economy” (DTI, 1998)

emphasised the role of government, universities and businesses in improving the

UK’s competitiveness, and drew attention to government’s ability to promote

enterprise and stimulate innovation by rewarding universities for strategies and

activities to enhance interaction with business. The white paper “Excellence and

Opportunity” (DTI, 2000) highlighted the crucial role of government in encouraging

the exploitation of knowledge and new technologies.

In the early 2000s, particular attention was paid to the regional dimension of

universities’ engagement with businesses and the community. The “Future of Higher

Education” white paper (DES, 2003) proposed a more regional focus for universities

to support economic development. In 2000 the government created a new Regional

Innovation Fund worth £50 million a year to enable Regional Development Agencies

(RDAs) to support clusters and incubators and networking among scientists,

entrepreneurs, managers and financiers. The Lambert Review (HM Treasury, 2003)

1 Lupton et al. (2013) estimate that expenditure in capital services in the period 1997-2010 grew by approximately 59 percentage points.

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emphasized that RDAs should be given targets to promote links between business and

university. The Fifth Parliamentary Report by the Select Committee on Science and

Technology (2003) recommended HEFCE not only to work with the RDAs, the

universities and other interested parties, but also to develop measures to assess the

effectiveness of knowledge transfer between universities and business, with a

particular focus on their regional dimension, to complement the national quality

measures for teaching and research. The report suggested the implementation of

appropriate metrics, to ensure “sustained commitment by HEIs to supporting business

so that they develop the motivation, capacity, capability and commitment to interact

professionally and effectively with regional development in all its breadth” (Select

Committee on Science and Technology, 2003, fifth report).

After the mid-2000s, the Governments’ aspirations for the role of universities in

supporting economic growth have broadened further. It has been recognized that not

all universities play the same role in supporting economic growth. The white paper

“Opportunity for all in a world of change” (DTI/DFES, 2005), recognised the crucial

role of universities in the economy as powerful drivers of innovation and change, but

claimed that different universities have different contributions to make: some as world

class centres of research excellence and players in global markets; others primarily as

collaborators with local businesses and communities, and with regional bodies.

Institutions must choose the role which best suits their strengths. Public funding

should encourage such choice, by providing incentives for institutions to become

more entrepreneurial, build closer links with business and the community, and have

proper arrangements for exploiting the results of their work. In line with this more

diverse approach to the nature of universities’ knowledge transfer activities, the

Sainsbury Review (HM Treasury, 2007) recommended that funds dedicated to

supporting university knowledge transfer should be spread more widely across the

sector, since different universities engage in different types of knowledge transfer

activities.

The role of Intellectual Property Rights (IPR) on commercialisation of research was

addressed in the Gowers Review (HM Treasury, 2006) and was enhanced further by

the Hargreaves Review (Hargreaves, 2011) which highlighted the importance for

universities and SMEs to realise the potential of IP especially copyright. The UKIPO

also joined the bandwagon by providing guidelines on Intellectual Asset Management

15

for universities. The Saraga report (2007) highlighted that focus of income generation

for universities on the part of the government and public funders may lead to an

overemphasis on IP from negotiations which may not be beneficial to the wider

economy.

The “Innovation Nation” white paper (DIUS, 2008) argued for the importance of

building a supporting ecosystem for university-industry interactions that involved also

the Research Councils, the Technology Strategy Board (TSB) and RDAs, universities

and businesses. Emphasis was increasingly placed on creating collaborative relations

and two-way exchange of knowledge as opposed to one-way knowledge transfer. The

Wilson Review (Wilson, 2012) emphasised the importance of adopting a holistic view

of collaboration between universities and business. It stated that there was a need to

assess the impact of the programme on actual knowledge transfer, which should not

be measured purely on the basis of economic gain but also consider policy

development (Wilson, 2012).

Over time, the regional focus was progressively abandoned. Following the publication

of the white paper “Local Growth: Realising Every Place’s Potential” (BIS, 2010),

and in parallel with the change to a Conservative-Liberal Democrats coalition

government, the RDAs were closed down (31 March 2012) and new business-led

Local Enterprise Partnerships (LEPs) between local authorities and businesses were

established. By April 2014 all areas of England are now covered by a LEP, taking the

total to 39 (BIS Committee, 2014). The Witty Review (BIS, 2013) highlighted the

importance of the LEP as an economic growth pathway, not only supporting the local

region where knowledge transfer is strongly encouraged, but more generally in the

UK. Moving away from the regional focus, funding allocation should support LEPs

partnering with local universities which align their distinctiveness with opportunity,

understanding the locality’s competitive advantage and leveraging the natural assets

of their co-location towards a seamless growth agenda (BIS, 2013). The publication

of BIS 2014 report on international benchmarking of the UK science and Innovation

system also offers a more comprehensive picture on the importance of university-

business collaboration in UK innovation ecosystem and addresses the importance of

the structures and incentives in innovation ecosystem evaluation.

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Summarizing, the analysis of government white papers and policy reviews dealing

with university knowledge transfer since the mid-1990s reveals that the government’s

policy objectives have progressively become broader and more ambiguous:

• In the late 1990s-early 2000s, the documents suggested that universities would

be expected to transfer knowledge to their local/regional communities. The

focus was mainly on technology transfer in the form of commercialization of

patented technology and direct contracts between universities and business,

focusing on science and engineering. This was also reflected in the

terminology used, as the most widely used term was “technology transfer”

(e.g. National Committee of Enquiry Into Higher Education, 1997; DTI,

1998).

• Since the mid-2000s, the term “knowledge transfer”, emphasizing the not

necessarily technological nature of the knowledge transferred by universities,

began to be used widely (e.g. DES, 2003; HM Treasury, 2003); the term in

fact encompasses a broader range of activities than the commercialization of

research results and the performance of research contracts. It was also

acknowledged that different institutions can play different roles in knowledge

transfer, with some producing research excellence and interacting with large

global companies, others providing skills and knowledge to their local

communities. The focus broadened from science and engineering to the entire

spectrum of academic disciplines, including the social sciences and the arts

and humanities. The limitations of patents as vehicles of knowledge transfer

were increasingly acknowledged.

• Since the late 2000s, the term “knowledge exchange”, which emphasizes the

bi-directional and collaborative nature of the process of interaction between

universities and businesses (or other stakeholders) began to emerge (e.g.

DIUS, 2008; Wilson, 2012 and BIS, 2013). This coincided with the adoption

of an even broader perspective, according to which universities were expected

to be part of complex ecosystems of innovation characterized by collaboration

and exchange among a variety of stakeholders (Andersen, Brinkley and

Hutton, 2011).

By way of illustration, Figure 1 shows the share of UK policy documents that include

the words “technology transfer”, “knowledge transfer” and “knowledge exchange”

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between 2008 and 2013 (data have been obtained by searching all policy documents

produced by UK government ministerial departments and other public bodies). Even

in this relatively short period, it can be seen how the former term has decreased in

importance while “knowledge transfer” and “knowledge exchange” have entered

progressively greater use. These concepts, and especially the latter, are more

comprehensive but also much more vague and ambiguous (with respect to the types of

activities that should be supported, the types of benefits that these activities should

generate, and who should primarily benefit from them), than the concept of

technology transfer which prevailed in the preceding years.

Figure 2: The relative importance of the words “technology transfer”, “knowledge transfer” and

“knowledge exchange” in UK policy documents

Source: Authors’ elaboration based on information available from

https://www.gov.uk/government/publications (last accessed July 2014).

The table reported in Appendix 1 summarizes the key policy documents discussed in

this section, their main purpose and objectives (where the documents deal with

broader higher education issues, we focused only on purposes and objectives relating

to university-industry knowledge transfer) and presents some comments on their

evolution over time.

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

55.00%

60.00%

2008 2009 2010 2011 2012 2013

% p

olic

y do

cum

ents

Technology transfer

Knowledge transfer

Knowledge exchange

18

4.3. The evolution of policy instruments: narrower scope

In parallel with the setting out of government aspirations in white papers and reviews,

several policy initiatives launched since the mid-1990s sought to improve links

between higher education and industry.

The University for Industry (Ufi) initiative, launched in 1998, was a promotional,

brokerage and commissioning agency which aimed to mobilise the expertise and

energy of government, business and education towards meeting the needs of the

market by providing people with information, and ensuring the availability of high

quality programmes and products (Grady and Pratt, 2000). Ufi's learning services

were delivered through Learndirect, a public-private partnership founded in 2000

which provided access to courses sponsored under the EU’s ADAPT programme

(Hillage et al., 2001)

In 1999, a package of measures called the Knowledge Exploitation Programme was

launched with the objective to support universities and publicly funded research

institutes to engage in various forms of knowledge transfer to business. In England,

these included:

(i) The Higher Education Reach-out to Business and the Community (HEROBAC)

Fund. Sponsored by DFES and DTI and allocated by HEFCE, the HEROBAC fund

initially was set at £60m over four years and was due to become a permanent third

stream of funding, specifically aiming to develop the capability of universities to

engage with business and the wider community, by putting into practice appropriate

organisational and structural arrangements.

(ii) The Science Enterprise Challenge (SEC). This initiative supported

entrepreneurially oriented education and training through networks of universities. It

supported innovation culture in universities, to make them more relevant to business.

Allocated through competition and managed directly by the government Office of

Science and Technology (OST), £45 million was made available over the period

1999-2004.

(iii) The University Challenge Seed Fund provided access to seed funds to exploit

science and engineering research outcomes and support the creation of university

spin-outs. The scheme was funded by Wellcome Trust, Gatsby Charitable Foundation

19

and the UK Government. Universities receiving the fund had to provide 25% of the

total fund from their own resources. £45 million was allocated in the first round of

the competition in 1999, and £15 million more in 2001.

In the early 2000s, HEFCE also introduced a monitoring system collecting

information about universities’ knowledge transfer activities, based on the HEBCI

survey. In the following, we show how the output information contained in the

HEBCI survey has progressively gained increasing importance in the context of

university knowledge transfer policy, while the content of the survey has become

focused on a narrower range of activities and quantitative indicators have taken on

greater importance. This has in turn allowed HEFCE to use the information provided

in the survey as a basis to build more streamlined formulas for its funds allocation

system.

The origins of the HEBCI can be traced back to the late 1990s, when, following the

introduction of HEROBAC, a need emerged to monitor the knowledge transfer

activities of universities. Some preliminary surveys, which formed the basis upon

which the HEBCI was eventually developed, had already been commissioned in the

mid to late 1990s (Howells, Nedeva and Georghiou, 1998), but their scope was

limited to relatively few universities. These surveys placed a strong emphasis on

qualitative information and had a strong focus on measuring regional interactions.

In order to systematise data collection, HEFCE was put in charge of carrying out a

comprehensive survey covering all higher education institutions in the UK. The first

edition of the survey, called Higher Education and Business Interaction (HEBI) was

launched in 2001, referring to the period 1999-2000. It was commissioned by HEFCE

to the Centre for Urban and Regional Development Studies, University of Newcastle

upon Tyne (Charles and Conway, 2001).

In 2003, the Select Committee on Science and Technology report suggested that the

measurement of university interaction with businesses should not only provide

incentives for HEIs to engage with business and society but also highlight the focus

activities that make a difference for economic development. To this end, the metrics

used should recognise that: (a) the interactions will be of many different types; (b)

engagement must not be constrained by regional boundaries; and (c) meaningful

assessment will require a long-term and, in part, subjective view. While these

20

recommendations were welcomed by HEFCE, in practice, however, the indicators

used in the survey have become progressively narrower especially starting from the

third edition of the survey, carried out in 2003 and referring to 2001/2002.

Figure 3 shows how the structure of the survey (now called Higher Education

Business and Community Interaction, HEBCI) changed drastically in 2002, with the

survey being split into two parts, one dedicated to the collection of qualitative

information about universities’ knowledge transfer infrastructures and strategies (part

A), and one dedicated to the collection of quantitative information on their knowledge

transfer activities (part B). The figure shows how the topics present in the first two

editions of the survey (1999-2002) were reallocated into two main sections (part A

and part B). Although the information collected starting from 2002 was initially not

too dissimilar from that collected in previous editions of the survey, in practice

collating all the quantitative information in a separate section made it easier to detach

it from qualitative information about the context in which it was generated, and we

can argue that this facilitated the transition toward a system in which the only part that

actually “matters” for policy implementation is the quantitative part.

Over time, there has also been a progressive change in the importance of the different

thematic areas measured in the survey. Figure 4 shows the relative importance of

different themes, measured on the basis of their weight in the survey (share of the

overall number of questions). In terms of relative importance, four main themes

gained ground: intellectual property, provision of facilities and equipment services,

and contract research and consultancy. Other themes declined in importance, albeit

slightly: strategic objectives, spinoff companies, and regeneration programmes. A

couple of themes appeared to lose considerable ground: infrastructure and policy, and

skills provision. The theme “other events”, having to do with social, community and

cultural engagement, was only introduced in 2001/2002 and, after a period of

increasing importance, it stabilized.

Therefore, even though policy documents increasingly encouraged a focus of a broad

set of knowledge transfer activities, emanating from a variety of academic disciplines,

in practice the survey has attributed progressively greater importance to a few types of

activities particularly likely to generate income to the university, many of which are

also particularly associated with technological and scientific subjects. The loss of

importance of regeneration programmes, spinoff companies and skills provision

21

themes reflects a shift away from the regional dimension of knowledge transfer, with

progressively greater importance attributed to the achievement of “excellence” on a

national scale rather than to the involvement in interactions with the local community.

The reduced focus on strategies and policies also suggests a shift away from more

intangible aspects of engagement and towards more tangible, quantifiable outputs.

Figure 3: Main changes in the structure of the HEBCI

Source: Authors’ own elaboration based on HE-BI and HEBCI questionnaires, available from

http://www.hefce.ac.uk/data/ (last accessed July 2014).

Table 2: Business & community services

Table 3: Regeneration & development

programmes

Table 4: Intellectual property (IP)

Institutional Strategy and Economic

Spin off firms

Consulting activities

Regeneration activity

Training and personnell links

Table 5: Social, community & cultural

Engagement:

HE- BI survey1999 - 2002

HE-BCI survey 2002-2011

01- Strategy

02- Infrastructure

03 - Intellectual property

04 - Social community cultural

05 - Regeneration

06 - Education CPD

Table 1: Research related activities

Collaborative research with business

Intellectual property

Part B: Quantitative

information on universities'

engagement in different

types of activities:

ONLY this part of the

HEBCI is used for HEIF

funding allocation

purposes

Part A: Qualitative

information on

universities' strategies,

infrastructures and

nature of engagement in

different types of

activities !

22

Figure 4: The relative importance of various thematic areas in the survey, over time



Also if we focus only on the quantitative part of the survey, as in Figure 5, we find

that the relative importance of various thematic areas has changed: rising importance

of intellectual property, provision of facilities and equipment services, consultancy

and contract research, and, again, progressive loss of importance of spinoff companies

and regeneration programmes.

If we consider the split between quantitative and qualitative indicators, Figure 6

clearly shows that over time, and in particular since 2002, the share of questions

collecting quantitative information has increased.

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

20.00%

1999

/200

0

2000

/01

2001

/02

2002

/03

2003

/04

2004

/05

& 2

005/

6

2006

/07

2007

/08

2008

/09

2009

/10

2010

/11

% q

uest

ions

Strategic objectives

Infrastructure and policies

Skills provision

Contract research

Consultancy

Facilities and equipment services


Spin off companies

Other events

Regeneration programmes

23

Figure 5: Focusing only on the quantitative indicators: The relative importance of various

thematic areas in the survey, over time



Figure 6: The growing importance of quantitative measures



0.00% 2.00% 4.00% 6.00% 8.00%

10.00% 12.00% 14.00% 16.00% 18.00% 20.00%

1999

/200

0

2000

/01

2001

/02

2002

/03

2003

/04

2004

/05

& 2

005/

6

2006

/201

1

% q

uest

ions

Skills provision

Contract research

Consultancy

facilities and equipment services


Spin off companies

Other events

Regeneration programmes

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1999

/200

0

2000

/01

2001

/02

2002

/03

2003

/04

2004

/05

& 2

005/

6

2006

/201

1

% q

uest

ions

Quantitative information:

Qualitative information

24

In parallel with the introduction of the HEBCI survey, a new stream of funding to

support university-industry knowledge transfer was announced following the

Government’s 2000 Spending Review, in order to continue and develop the work of

the HEROBAC initiative: the Higher Education Innovation Fund (HEIF)2 . The HEIF

was launched in 2001/2 as a partnership between DTI/OST, HEFCE and the

Department for Education and Skills (DFES). This established a third stream of

funding, to sit alongside the core funding to university institutions for research, and

for learning and teaching. The HEIF was supposed to facilitate a more strategic

approach to supporting universities, some of which attributed more importance to

supporting local industry and to other focus areas, than to basic research (Select

Committee on Science and Technology, 2003). The introduction of the HEIF also

brought about a streamlining of the set of initiatives targeting universities’ knowledge

transfer funding, as in 2003 the activities originally funded by the Science Enterprise

Challenge and University Challenge Seed Fund were brought within the remit of the

HEIF.

The following figure shows the evolution of the amount of funding dedicated to the

HEIF in England since its inception in 2001. After a marked increase in funding

between 2004 and 2008, the fund has later stabilized on a lower amount of just under

£120 million per year.

2 Each region in the UK has its own agencies that fund activities to support universities knowledge transfer initiatives: in England, the Higher Education Funding Council for England (HEFCE) manages the Higher Education Innovation Fund (HEIF); in Wales, the Higher Education Funding Council for Wales allocates the Innovation and Engagement Fund; in Scotland, the Scottish Funding Council is responsible for the Knowledge Transfer grant; and finally in Northern Ireland, the Department for Employment and Learning Northern Ireland (DELNI) manages the Higher Education Innovation Fund (HEIF).

25

Figure 7: The evolution of HEIF: funding allocation

Source: Authors’ elaboration based on data from HEIF reports, available from

http://www.hefce.ac.uk/whatwedo/kes/heif/ (last accessed July 2014).

Besides the consolidation of various funds into a single stream, the decade following

the introduction of HEIF has also seen a progressive streamlining of the allocation

process. First, while initially funds were allocated to universities competitively, on the

basis of the proposals that they presented, since 2006 a formula-based system

calibrated according to the universities’ performance in knowledge transfer has been

implemented. This has been justified in terms of a transition from capability building

to performance-based funding. The funds, allocated competitively, in the first period

of the HEIF (HEIF 1 and HEIF 2), were supposed to help institutions build their

knowledge transfer capability, by setting up appropriate infrastructures and

developing competences; while the switch to performance based funding was justified

on the basis of the intention to reward and encourage excellence in knowledge

transfer alongside research and teaching (HEFCE, 2011).

26

Second, the criteria for measuring performance have become progressively narrower:

while HEIF 3 and 4 introduced formula-based funding, this constituted only part of

the overall allocation with the remaining still being allocated competitively. Since

HEIF 5, the allocation is entirely formula-based.

Since the introduction of formula funding in HEIF 3, the relationship between the

HEBCI survey and the HEIF has strengthened, because the information collected in

the HEBCI provides the basis for the formula calculation. Moreover, while in HEIF 3

the formula included some element of evaluation of performance in activities not

measured by income, starting from HEIF 4 the formula is entirely based on the

income that universities accrued from knowledge transfer3, as shown in Table 1, and

the information used for the computations is entirely sourced from the HEBCI.

Table 1: Evolution of HEIF allocation mechanism

Components Competitive Formula Formula

Year Fund Potential and capacity building

Activities not best measured by income

External income

2001-2004 HEIF 1 100% 2004-2006 HEIF 2 100% 2006-2008 HEIF 3 45% 10% 45% 2008-2011 HEIF 4 40% 60% 2011-2015 HEIF 5 100%



Third, over time a more stringent approach to funds allocation has been used, from

granting the funds lump sum (HEIF 1-3) to administering the allocation yearly (HEIF

4, 5). This somehow requires a more strategic approach for universities to plan for

their knowledge transfer activities within the specific HEIF period. There has also

been a move towards greater concentration of funds, with an increase in the maximum

award received by each university (£2.85 million for HEIF 5) and the introduction of

a threshold allocation where only university receiving more than £250,000 knowledge

3 The 100% formula allocation only applies to English universities; the shares of funds allocated through formula are 80% in Northern Ireland, 75% in Wales and 92% in Scotland. Nonetheless, the broad trends described here apply to the policies implemented in all four UK nations.

27

transfer income are eligible to receive their HEIF funds. Details of the conduct of

HEIF allocation are summarised in Table 2.

Table 2: Evolution of the HEIF funding allocation system

HEIF 1 HEIF 2 HEIF 3 HEIF 4 HEIF 5 Year 2001-

2004 2004-2006 2006-2008 2008-2011 2011-2015

Total allocation

£77 million

£187 million

£238 million £396 million

£450 million

Notes: Up to an additional £20 million to fund a third and fourth year of the 22 Centres for Knowledge Exchange, provided they show satisfactory performance

A fifth and final allocation of £8 million is made available for existing Centres for Knowledge Exchange for the academic year 2008-09

Minimum allocation

£250,000 overall

£200, 000 overall

£200, 000 overall £100, 000 per year

No minimum allocation, but move to an external income threshold allocation.

Maximum allocation

£2,400,000 £3,000,000 250% of the previous allocation

£2,850,000

Other constraints

No institution will receive less than 75 per cent of its previous allocation under HEIF 2.

Each HEI is guaranteed 80% of their previous allocation

Maximum allocation constrained to 50% increase No HEI sees its allocation drop more than 50%

Threshold for participation in the HEIF funding scheme

None None None None £250,000 of knowledge transfer income



28

Summarizing, the instruments used to implement policies in support of universities’

knowledge transfer engagement have progressively been narrowed down in scope,

through several processes:

• Progressive focusing of the set of indicators used to collect information about

universities’ knowledge transfer activities on a narrower range of activities,

and increased importance of quantitative indicators

• Increased importance of the indicators emerging from the survey as a tool to

drive funds allocation, through:

o Merging of different funds into a single funding stream;

o Increased allocation of funding through a formula based system rather

than a competitive system;

o Progressive simplification of the formula used, most recently including

only income from knowledge transfer.

The figure in Appendix 2 shows the parallel evolution of policy objectives and

implementation instruments along a timeline that indicates the main events in

chronological order.

5. Conclusions

The case of the policies in support of university knowledge transfer in the United

Kingdom illustrates how, over time, a growing gap can open up between the

government’s increasingly broad and ambiguous objectives and the implementation

agencies’ use of instruments that are increasingly narrow in scope. We propose a

framework that captures the attributes that may enable us to evaluate how the growing

gap between policy aspirations and implementation occurs. We highlight that when

dealing with complex issues, the definition of policy objectives may tend towards

greater breadth and ambiguity over time, as increasing information becomes available

and as the number of stakeholders involved expands. This often results in objectives

being expressed in increasingly broad, vague and abstract terms, as the government

attempts to reconcile different and perhaps conflicting perspectives and interests. The

use of ambiguous language can also facilitate decision making on the part of the

implementation agencies by making it difficult to assess whether the policy

29

instruments adopted are actually supporting the objectives or not. At the same time,

the implementation builds upon instruments that are increasingly focused on the

achievement of measurable outputs, whose scope may narrow over time, for increased

practicality, to economize on financial and cognitive resources, and to increase

legitimacy by using indicators that appear objective and uncontroversial.

In terms of policy implications, it must be stressed that this study did not aim to assess

the relative merits of the government’s policy aspirations or of the policies

implemented as a consequence; rather, the objective has been to first and foremost

highlight the presence of a gap between them, and to explore the possible processes at

work. Increasing awareness of the factors that cause this gap could be useful in order

to understand when and where such gaps may occur. This may enable the parties to

then confront the issue, rather than obscure it behind abstract and vague objectives, on

the one hand, and the use of supposedly “objective” indicators, which however are

unrelated to those objectives, on the other.

Addressing the gap itself would require careful consideration, commitment and open

dialogue on the part of the parties involved. Perhaps one way to begin to address it

requires policymakers to adopt a “system thinking” approach and rely upon flexible

and versatile instruments, as Kapsali (2011) has highlighted in her work on the

interdependencies between policy objectives and implementation instruments. In

complex unpredictable contexts, flexibility in achieving a goal is better supported by

the concept of equifinality (Gresov and Drazin, 1997), by having different possible

trajectories–paths to reach the goal. By doing so, Kapsali (2011) explained that the

elements of implementation design can be pieced together into a holistic picture of

what has been aspired through the policy objective. Greater consistency between

policy objectives and implementation would be obtained not only by mixing different

instruments through “policy-mix” approach, especially for a broad policy target

(Nauwelaers et al, 2009), but also by better clarifying the rationales behind the

combinations (Flanagan et al., 2011). More generally, it would be important for

implementation agencies to clarify the characteristics and objectives of the

implementation mechanism (Dolfsma and Seo, 2013) by providing some insights on

the relevant level of differentiation between the instruments and how they may be

coupled with the structure of the policy objectives (Bach et al, 2014).

30

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